The ability to efficiently metabolize administered carbohydrate depends upon two factors: the ability of the pancreatic beta-cells to secrete insulin in response to a glucose stimulus (pancreatic responsivity), and insulin's ability to stimulate glucose metabolism by the tissues (insulin sensitivity). A substantial deficiency in either one or both these factors can lead to glucose intolerance and diabetes mellitus, and the ability to understand the etiology of diabetes requires methods for measuring the quantitative contribution of each. Using a combined experimental/mathematical approach, we have developed the "minimal modeling method" for quantifying pancreatic responsivity and insulin sensitivity in vivo from the dynamic glucose and insulin responses during the intravenous glucose tolerance test (IVGTT). Using the method, we have demonstrated that the intact organism has the capacity to balance pancreatic responsivity against insulin sensitivity. An increase in one factor is automatically compensated by a decrease in the other and the ability to dispose of glucose (glucose tolerance) remains optimized under normal conditions. This proposal is to experimentally validate our "minimal model" method, and utilize it to examine the abilities and limitations of the intact conscious dog model to optimize glucose tolerance under various experimental conditions (dietary changes, experimental diabetes, sulfonylurea therapy, exercise). We shall investigate the signals responsible for the cross-compensation of insulin sensitivity and pancreatic responsiveness, as well as the specific tissue changes which may be involved. We will investigate the specific defects which may result in metabolic disease states. We believe that the methodology we developed represents a physiologically-based approach to the evaluation of glucose tolerance which can have direct clinical application. Examination of the factors contributing to glucose tolerance in the experimental situation will yield a new, mechanistic view of glucose metabolism which can ultimately place metabolic diagnosis and therapy on a quantitative, scientific footing.